In the rapidly expanding Information Age, relational database systems (RDBMSs) have been implemented to handle many large-scale applications. As the databases grow larger and larger, processing efficiency may erode. Many systems support lossless compression methods such as null suppression and dictionary encoding on physical design structures such as clustered and non-clustered indexes. Depending on the compression method and the distribution of values in the index, a compressed index may utilize only a small fraction of the storage space otherwise occupied by an uncompressed index. For decision support queries which frequently scan large indexes, compression may provide reduced input/output (I/O) overhead. However, while some compression strategies may improve system performance, others may hamper the performance.